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AN ASPECT QUERY LANGUAGE MODEL BASED ON QUERY DECOMPOSITION AND HIGH‐ORDER CONTEXTUAL TERM ASSOCIATIONS
Author(s) -
Song Dawei,
Huang Qiang,
Bruza Peter,
Lau Raymond
Publication year - 2012
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00407.x
Subject(s) - computer science , query expansion , query language , query optimization , rdf query language , sargable , relevance (law) , relevance feedback , term (time) , web query classification , information retrieval , query by example , cross language information retrieval , language model , data mining , association rule learning , web search query , natural language processing , artificial intelligence , search engine , physics , image retrieval , quantum mechanics , political science , law , image (mathematics)
In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high‐order and context‐sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high‐order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state‐of‐the‐art query language models namely the Relevance Model and the Information Flow model.